{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import collections\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "  sys.path.append(\"../\") \n",
    "from lib.envs.cliff_walking import CliffWalkingEnv\n",
    "from lib import plotting\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "env = CliffWalkingEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class PolicyEstimator():\n",
    "    \"\"\"\n",
    "    Policy Function approximator. \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, learning_rate=0.01, scope=\"policy_estimator\"):\n",
    "        with tf.variable_scope(scope):\n",
    "            self.state = tf.placeholder(tf.int32, [], \"state\")\n",
    "            self.action = tf.placeholder(dtype=tf.int32, name=\"action\")\n",
    "            self.target = tf.placeholder(dtype=tf.float32, name=\"target\")\n",
    "\n",
    "            # This is just table lookup estimator\n",
    "            state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\n",
    "            self.output_layer = tf.contrib.layers.fully_connected(\n",
    "                inputs=tf.expand_dims(state_one_hot, 0),\n",
    "                num_outputs=env.action_space.n,\n",
    "                activation_fn=None,\n",
    "                weights_initializer=tf.zeros_initializer)\n",
    "\n",
    "            self.action_probs = tf.squeeze(tf.nn.softmax(self.output_layer))\n",
    "            self.picked_action_prob = tf.gather(self.action_probs, self.action)\n",
    "\n",
    "            # Loss and train op\n",
    "            self.loss = -tf.log(self.picked_action_prob) * self.target\n",
    "\n",
    "            self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "            self.train_op = self.optimizer.minimize(\n",
    "                self.loss, global_step=tf.contrib.framework.get_global_step())\n",
    "    \n",
    "    def predict(self, state, sess=None):\n",
    "        sess = sess or tf.get_default_session()\n",
    "        return sess.run(self.action_probs, { self.state: state })\n",
    "\n",
    "    def update(self, state, target, action, sess=None):\n",
    "        sess = sess or tf.get_default_session()\n",
    "        feed_dict = { self.state: state, self.target: target, self.action: action  }\n",
    "        _, loss = sess.run([self.train_op, self.loss], feed_dict)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class ValueEstimator():\n",
    "    \"\"\"\n",
    "    Value Function approximator. \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, learning_rate=0.1, scope=\"value_estimator\"):\n",
    "        with tf.variable_scope(scope):\n",
    "            self.state = tf.placeholder(tf.int32, [], \"state\")\n",
    "            self.target = tf.placeholder(dtype=tf.float32, name=\"target\")\n",
    "\n",
    "            # This is just table lookup estimator\n",
    "            state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\n",
    "            self.output_layer = tf.contrib.layers.fully_connected(\n",
    "                inputs=tf.expand_dims(state_one_hot, 0),\n",
    "                num_outputs=1,\n",
    "                activation_fn=None,\n",
    "                weights_initializer=tf.zeros_initializer)\n",
    "\n",
    "            self.value_estimate = tf.squeeze(self.output_layer)\n",
    "            self.loss = tf.squared_difference(self.value_estimate, self.target)\n",
    "\n",
    "            self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "            self.train_op = self.optimizer.minimize(\n",
    "                self.loss, global_step=tf.contrib.framework.get_global_step())        \n",
    "    \n",
    "    def predict(self, state, sess=None):\n",
    "        sess = sess or tf.get_default_session()\n",
    "        return sess.run(self.value_estimate, { self.state: state })\n",
    "\n",
    "    def update(self, state, target, sess=None):\n",
    "        sess = sess or tf.get_default_session()\n",
    "        feed_dict = { self.state: state, self.target: target }\n",
    "        _, loss = sess.run([self.train_op, self.loss], feed_dict)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def reinforce(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\n",
    "    \"\"\"\n",
    "    REINFORCE (Monte Carlo Policy Gradient) Algorithm. Optimizes the policy\n",
    "    function approximator using policy gradient.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        estimator_policy: Policy Function to be optimized \n",
    "        estimator_value: Value function approximator, used as a baseline\n",
    "        num_episodes: Number of episodes to run for\n",
    "        discount_factor: Time-discount factor\n",
    "    \n",
    "    Returns:\n",
    "        An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
    "    \"\"\"\n",
    "\n",
    "    # Keeps track of useful statistics\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))    \n",
    "    \n",
    "    Transition = collections.namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n",
    "    \n",
    "    for i_episode in range(num_episodes):\n",
    "        # Reset the environment and pick the first action\n",
    "        state = env.reset()\n",
    "        \n",
    "        episode = []\n",
    "        \n",
    "        # One step in the environment\n",
    "        for t in itertools.count():\n",
    "            \n",
    "            # Take a step\n",
    "            action_probs = estimator_policy.predict(state)\n",
    "            action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            \n",
    "            # Keep track of the transition\n",
    "            episode.append(Transition(\n",
    "              state=state, action=action, reward=reward, next_state=next_state, done=done))\n",
    "            \n",
    "            # Update statistics\n",
    "            stats.episode_rewards[i_episode] += reward\n",
    "            stats.episode_lengths[i_episode] = t\n",
    "            \n",
    "            # Print out which step we're on, useful for debugging.\n",
    "            print(\"\\rStep {} @ Episode {}/{} ({})\".format(\n",
    "                    t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\"\")\n",
    "            # sys.stdout.flush()\n",
    "\n",
    "            if done:\n",
    "                break\n",
    "                \n",
    "            state = next_state\n",
    "    \n",
    "        # Go through the episode and make policy updates\n",
    "        for t, transition in enumerate(episode):\n",
    "            # The return after this timestep\n",
    "            total_return = sum(discount_factor**i * t.reward for i, t in enumerate(episode[t:]))\n",
    "            # Calculate baseline/advantage\n",
    "            baseline_value = estimator_value.predict(transition.state)            \n",
    "            advantage = total_return - baseline_value\n",
    "            # Update our value estimator\n",
    "            estimator_value.update(transition.state, total_return)\n",
    "            # Update our policy estimator\n",
    "            estimator_policy.update(transition.state, advantage, transition.action)\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 14 @ Episode 2000/2000 (-15.0)"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n",
    "policy_estimator = PolicyEstimator()\n",
    "value_estimator = ValueEstimator()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    # Note, due to randomness in the policy the number of episodes you need to learn a good\n",
    "    # policy may vary. ~2000-5000 seemed to work well for me.\n",
    "    stats = reinforce(env, policy_estimator, value_estimator, 2000, discount_factor=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAFZCAYAAAD+cdmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1049dbc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAFZCAYAAAAGkSJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWXNHVvoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d886ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAFZCAYAAADpU0lNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d915be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x1049dbc18>,\n",
       " <matplotlib.figure.Figure at 0x10d886ef0>,\n",
       " <matplotlib.figure.Figure at 0x10d915be0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting.plot_episode_stats(stats, smoothing_window=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
